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Fraud Detection and Analysis for Insurance Claim using Machine Learning

Fraud Detection and Analysis for Insurance Claim using Machi...

引用

2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2022

作者： Urunkar, Abhijeet Khot, Amruta Bhat, Rashmi Mudegol, Nandinee Walchand College of Engineering Dept. of Computer Science and Engineering Sangli India Walchand College of Engineering Dept. of Information Technology Sangli India St. John College of Engineering and Management Dept. of Computer Engineering Palghar India

ISBN: (纸本)9781665449403

Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library. © 2022 IEEE.

关键词： Insurance

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High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture

High-Performance Image Restoration with SpectraNet: A Lightw...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Patnaik, Nitesh Raj, Rishi Singh, Piyush Kumar Mahapatra, Shreekara Pani, Debashis Singh, Ravi Shankar Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar751024 India Dept. of Computer Science Central University of South Bihar Bihar 824236 India Dept. of Computer Science and Engineering Indian Institute of Technology BHU Varanasi221005 India

Deep learning-based image restoration faces significant challenges when deployed on resource-constrained platforms due to the computational demands and large number of parameters in existing models. The high computational load and extensive memory requirements make it difficult to implement these models on devices with limited processing power and storage capacity, such as mobile phones and embedded systems. Additionally, maintaining real-time performance while ensuring high-quality image restoration is a critical challenge, as traditional deep learning models often fail to meet the stringent latency and efficiency requirements of such platforms. This paper introduces a new, lightweight convolutional neural network (CNN) architecture tailored for efficient pixelation detection and restoration. Our approach combines a pre-trained MobileNetV2 with finetuning for pixelation detection, and SpectraNet, a architecture incorporating depth-wise separable convolutions for image restoration. The proposed architecture was trained on the HQ-50k dataset, with 70% of the data used for training and 30% for testing. It achieved a Peak Signal-to-Noise Ratio (PSNR) of 17-23, outperforming 10 state-of-the-art architectures in both efficiency and image quality. This design addresses the dual problems of high computational load and extensive memory usage, prioritizing real-time performance and resource efficiency. The proposed architecture is ideal for deployment on mobile and embedded devices since it maintains high accuracy while significantly reducing the number of trainable parameters. Our findings advance the feasibility of deploying advanced image restoration techniques in real-world, resource-constrained environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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Integrating Mixed Reality and Body Weight Support Technology for Immersive Pediatric Rehabilitation 46

Integrating Mixed Reality and Body Weight Support Technology...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： McCarty, Tristan Surender, Smrithi Shu, Cary Huang, Zijian Chen, Jiasi Kokkoni, Elena University of California Riverside Dept. of Bioengineering RiversideCA92521 United States University of Michigan Dept. of Electrical Engineering and Computer Science Ann ArborMI48109 United States

ISBN: (纸本)9798350371499

Interventions involving the use of assistive technology have the potential to enhance motor outcomes for children with mobility challenges. Keeping up with the intervention, however, is challenging due to decreased engagement and motivation toward practicing the training tasks. To address these challenges, this work focuses on the development of a pediatric motor training environment that combines dynamic body weight support and mixed reality technology. Both components allow for personalized, variable motor practice in an environment enriched with gamified activities. This paper describes the system configuration alongside five game scenarios that were developed to target various motor skills, such as standing, walking, squatting, and reaching. Preliminary results on the mixed reality environment performance as well as next steps in this work are discussed. © 2024 IEEE.

关键词： Pediatrics

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A Hybrid Ant Colony Optimization for Parkinson's Disease Classification Based on Synthetic Minority Oversampling and Adaptive Synthetic Techniques 5

A Hybrid Ant Colony Optimization for Parkinson's Disease Cla...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Saheed, Yakub Kayode Abdulganiyu, Oluwadamilare Harazeem Abdulsalam, Mustapha Mustapha, Musa Olivier, Mekila Mbayam Majikumna, Kaloma Usman American University of Nigeria School of IT and Computing Yola Nigeria University of Maiduguri Maiduguri Nigeria Skyline University Dept. of Microbiology Kano Nigeria Yobe State University Dept. of Computer Science Yobe Nigeria Euromed University of Fes Fes Morocco University of Maiduguri Dept. of Computer Engineering Maiduguri Nigeria

ISBN: (纸本)9798350368710

Parkinson's disease (PD) is a neurological ailment of the nervous system, first characterized in 1817. Around 90% of PD patients experience speech problems. Therefore, early detection and identification of PD are critical for disease progression and treatment. This study proposes a unique approach for detecting PD using features extracted from speech signals of patients with PD. We categorize the hybrid machine learning (ML) technique into two stages: (i) pre-processing of data, followed by (ii) hybrid ant colony optimization (ACO), and (iii) classification using the extra tree, cat boost, and Xgboost. However, the PD data set's class distribution is skewed (unbalanced). Therefore, we applied the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic technique (ADASYN) to balance this unbalanced data. We classified the PD dataset using the hybrid ACO with extra tree, hybrid ACO with cat boost, and hybrid ACO with Xgboost classification methods after transforming it to a balanced majority class. The suggested hybrid methods, which combine SMOTE-ACO+extra tree, and ADASYN-ACO+Extra tree gave classification accuracy rates of 99.28%, and 95.72%. The results indicated that the proposed hybrid ACO technique produced promising results in PD classification. © 2024 IEEE.

关键词： Ant colony optimization

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Enhancing Blind Communication With Voice-Assisted Email

Enhancing Blind Communication With Voice-Assisted Email

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Expert Clouds and Applications (ICOECA), International Conference on

作者： C. Sasikala Rupalakshmi M Sameer Ahammad S Meghana U Harshad Ali M Dept. Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Anantapur India Dept. Computer Science and Engineering(Data Science) Srinivasa Ramanujan Institute of Technology Anantapur India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Considering the key role of the internet in daily life, accessibility becomes crucial, especially for visually impaired individuals who heavily rely on it for accessing information. However, a significant portion of text materials on the internet remains inaccessible to blind users, limiting their opportunities for development and inclusion in various communities worldwide. Leveraging audio virtual environments, predominantly through screen reader technology, offers a notable advantage for blind individuals in accessing online applications like emails. This study presents a new design aimed at facilitating the email retrieval for blind users with the utilization of speech-to-text and text-to-speech technologies. This approach is particularly suitable for individuals with average human capabilities and a limited level of education, as it relies solely on interactive voice response for input, ensuring effective functionality for the proposed system.

关键词： Productivity Navigation Virtual environments User interfaces Electronic mail Text to speech Standards

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Pattern Recognition based Smart Billing System for Water Consumption 7

Pattern Recognition based Smart Billing System for Water Con...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Alagarsamy, Saravanan Sreshta, Dubba Rani, Dondapati Usha Reddy, Dudda Sai Yashwanth Nidita, Pasapula Sai, Boddapu Niteesh Satya Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Tamil Nadu Krishnankoil India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The majority of the water used depends upon the water billing, especially after receiving the invoice from users. While performing the reading taken in the manual procedure, a lot of issues are arising not properly reading is considered. Many complaints are received that for the less usage, the huge amount of bill is consumed due to the reason of calculation errors or late processing of delivering the invoice to the users. These particular issues motivated us to create an automated method for calculating the water bill for usage. The user will get an immediate notification after the water usage and day-to-day updates related to how many units are consumed/usage. Users need to register in the portal through the mobile number with the address. The device inserted in a meter is used to capture the units consumed an invoice is generated to the user and the customers are having provision to pay the bill through online. The users can see all the updates through the portal and immediate SMS will be generated to the customers. © 2022 IEEE.

关键词： Pattern matching

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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Relative Position Estimation Using Cross Filter for Rolling-Shutter Cameras

Relative Position Estimation Using Cross Filter for Rolling-...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Daisuke Sekimoto Koji Kamakura Masayuki Kinoshita Takaya Yamazato Dept.of Computer Science Chiba Institute of Technology Narashino Japan Institute of Liberal Arts and Sciences Nagoya University Nagoya Japan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

We propose an estimation method of light emitting diode(LED) position with a cross filter (CF) for rolling-shutter (RS) camera. The CF is set in front of the camera lens so that incident light can be scattered into two straight lines extending ±π/4 from the horizontal row of the CMOS image sensor (IS). With the geometry of a right-angled isosceles triangle, our method makes it possible to estimate the coordinates of the LED in the IS plane when the scattered light is captured at two points in a scanline. Furthermore, our method makes it possible rows by rows when multiple LEDs are used or when they are moving. Without the CF, estimation is done only once per frame when the scan of the sequentially activated operation of the RS camera reaches a row that has just captured the whole image of the target LED, and the estimated coordinates are not updated in that frame. Experiments verify that our estimation method works with the estimation error less than 5pixels, and to extend the region of a frame in which estimated coordinates are output, even in a moving environment, while the conventional method (without CF) obtains the estimated coordinates only once when the scan reaches a row that captures entire LED.

关键词： Geometry Estimation error CMOS image sensors Light emitting diodes Cameras Global communication Lenses

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A Secure Automated Level Crossing and Train Detection System for Bangladesh Railway

A Secure Automated Level Crossing and Train Detection System...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Ahmed, Khandakar Rabbi Hossain, Md Alomgir Akter, Afroza Akthar, Lamia International University of Business Agriculture and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

This research focuses on secure automating the level crossing and tracking the train using a microcontroller and a train detection system. An important issue in Bangladesh's railway transportation is the level crossings bar controlling system. The bars are presently manually adjusted in Bangladeshi railways. As soon as the train arrives, the bar lineman will know. On the basis of that data, they will close and re-open the A lineman's irresponsibility is a big issue. An automated railway gate control system can prevent this by automatically closing or opening the bar as a train arrives or departs. In normal operation, the sensors close the bars when a train is spotted and open them after it departs. Trains, however, will be GPS-tracked. The passengers and railroad authorities could track it. Each train has its own code. With the code, passengers can communicate with the train tracking system. In order to avoid collisions, the authority and train driver might be alerted. © 2022 IEEE.

关键词： Railroad crossings

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement 3

Comparative Study of Implementation of Very Deep Super Resol...

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3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

作者： Ganesan, P. Ravichandran, M. Sathish, B.S. Joseph, L.M.I.Leo Sajiv, G. Murugesan, R. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Saveetha School of Engineering Simats Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350358742

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results. © 2023 IEEE.

关键词： Medical imaging

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